graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. This document outlines the setup process and provides a basic example of using the client. Ingest anything from Slack, Discord, websites, Google Drive, email, Jira, Linear or GitHub into a Graphlit project - and then search and retrieve relevant knowledge within an MCP client like Cursor, Windsurf, Goose or Cline. Your Graphlit project acts as a searchable, and RAG-ready knowledge base across all your developer and product management tools.
Ads
Overview
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. This document outlines the setup process and provides a basic example of using the client. Ingest anything from Slack, Discord, websites, Google Drive, email, Jira, Linear or GitHub into a Graphlit project - and then search and retrieve relevant knowledge within an MCP client like Cursor, Windsurf, Goose or Cline. Your Graphlit project acts as a searchable, and RAG-ready knowledge base across all your developer and product management tools.
Capabilities
- Ingest content from tools like Slack, Jira, or GitHub
ask your assistant to ingest messages, issues, or files from connected data sources into your Graphlit project.
- Search across all ingested knowledge
use `Query Contents` or `Retrieve Relevant Sources` to find documents, messages, web pages, and transcripts.
- Ask questions against your project
prompt the LLM with `Prompt LLM Conversation` to get RAG-based answers grounded in your ingested content.
- Crawl and search the web
use built-in `Web Crawling` and `Web Search` tools to pull in external pages or podcast results.
- Extract structured data from text
apply `Extract Structured JSON from Text` to pull typed information out of unstructured content.
- Manage collections and feeds
create, update, or delete collections and feeds, and check ingestion status with `Is Feed Done?` or `Is Content Done?`.
Installation
npx -y graphlit-mcp-server
Configuration
{
"mcpServers": {
"graphlit-mcp-server": {
"command": "npx",
"args": [
"-y",
"graphlit-mcp-server"
]
}
}
}Paste into ~/.cursor/mcp.json or .cursor/mcp.json
Tags
- cloud-infrastructure
- awesome-list
Related MCP servers
Knowledge & Memory
Memory MCP Server
Persist assistant memory as a lightweight knowledge graph across sessions.
2 tools · create_entities, search_nodes
Knowledge & Memory
OpenLore
Sponsored by <a href="https://oiya.ai/?utm_source=github&utm_medium=referral&utm_campaign=openlore&utm_content=sponsor_logo"><img src="assets/oiya-logo.svg" alt="Oiya" height="24" align="absmiddle"></a> OpenLore is a minimal, extensible, agent-native knowledge base that keeps shared context current and inspectable. AI coding agents already know how to explore files with ls, cat, grep, find, pipes, and shell loops. OpenLore gives them that same interface over SSH, backed by your documentation instead of a real machine.
5 tools · Manage identity-scoped access, publish
Knowledge & Memory
liza-studio/skillmem
Self-improving skill memory for coding agents: skills that help get reinforced, unused ones fade on an Ebbinghaus decay curve. $0 write path (SQLite FTS5 + local ONNX embeddings, no LLM calls), bilingual EN/RU hybrid search, SHA256 tamper-evident history, deterministic LongMemEval benchmark in-repo (hit@5 0.871). 8 mem_* tools; deep Claude Code hook integration optional. `pip install skillmem`
Knowledge & Memory
yubinkim444/repo-memory
Shared, git-tracked memory for AI agents on the same codebase. Stores facts/decisions/gotchas in a `.ai-memory/` directory you commit to the repo — sync is git, no DB, no daemon. What one Claude/Cursor/Cline learns, the next one picks up automatically across users and machines. 5 MCP tools (`get_repo_memory`, `add_fact`, `list_facts`, `add_decision`, `add_gotcha`). Install: `uvx repo-memory-mcp --repo /path/to/repo`.
Knowledge & Memory
Mnemosyne-OS/Mnemosyne-Neural-OS
Memory the human governs: vaults separated by domain (code, notes, journal) on your own machine, hybrid retrieval (BM25 fused with vectors by RRF), and a declared vault list, so a vault left out cannot be read by mistake. Also reads what the OTHER coding agents on this machine wrote to disk, and flags when two are live in the same git worktree. Needs the Mnemosyne OS desktop app running. `npx -y @mnemosyne_os/mcp`
Knowledge & Memory
kunickiaj/codemem
Persistent coding memory across sessions, machines, and teammates. Provides MCP tools for searching and managing memories, optional cross-machine sync, and project sharing. OpenCode, Claude Code, and Codex integrations add automatic capture and context injection.
Knowledge & Memory
kskurtveit/echocache
Cache expensive LLM results — HTTP-style freshness (TTL / stale-while-revalidate) plus semantic recall of related past answers
Knowledge & Memory
pipeshub-ai/mcp-server
Permission-aware enterprise search and RAG that gives agents context from your business apps (Google Workspace, Microsoft 365, Slack, Jira, and others). Streamable HTTP or local stdio (`npx @pipeshub-ai/mcp`). Official registry: `io.github.pipeshub-ai/mcp`.